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authorFlorian Jung <flo@windfisch.org>2015-08-30 21:08:57 +0200
committerFlorian Jung <flo@windfisch.org>2015-08-30 21:08:57 +0200
commitc383083c3e41ae252f8e66d0b13795c5536b7b01 (patch)
treedd158c89bf6e83203d9e5aa00e016599cca4246e /stats.py
parentc3623e098edce070b6e89f2c71638cec2efdd694 (diff)
analyze_{virus_sizes,remerge}() functions
Diffstat (limited to 'stats.py')
-rw-r--r--stats.py22
1 files changed, 20 insertions, 2 deletions
diff --git a/stats.py b/stats.py
index 4827d8a..42ee0ea 100644
--- a/stats.py
+++ b/stats.py
@@ -388,13 +388,17 @@ class Stats:
def analyze_distances(self, celltype):
ds = [v[0] for v in self.data.eject_distlogs[celltype]]
+ ns = [v[4] for v in self.data.eject_distlogs[celltype]]
try:
mean, stddev = fit_gaussian(ds)
+ meann, stddevn = fit_gaussian(ns)
except:
mean, stddev = "???", "???"
+ meann, stddevn = "???", "???"
- print(celltype+" eject/split distances: mean = "+str(mean)+", stddev="+str(stddev)+", ndata="+str(len(ds)))
+ print(celltype+" eject/split distances: mean = "+str(mean) +", stddev ="+str(stddev) +", ndata="+str(len(ds)))
+ print(celltype+" meann = "+str(meann)+", stddevn ="+str(stddevn))
#a,b = numpy.histogram(ds, bins=100)
#midpoints = list(map(lambda x : (x[0]+x[1])/2, zip(b, b[1:])))
@@ -408,5 +412,19 @@ class Stats:
#quant = quantile(list(map(lambda v : abs(v-midpoints[maxidx]), ds)), q/100)
#print("\t"+str(q)+"% of values lie have a distance of at most "+str(quant)+" from the maximum")
- print("\t75%% of the values lie in the interval %.2f plusminus %.2f" % find_smallest_q_confidence_area(ds, 0.75))
+ print("\t75%% of the distances lie in the interval %.2f plusminus %.2f" % find_smallest_q_confidence_area(ds, 0.75))
+ print("\t75%% of the flight lengths lie in the interval %.2f plusminus %.2f" % find_smallest_q_confidence_area(ns, 0.75))
print("")
+
+ def analyze_virus_sizes(self):
+ print("\nI've seen the following %d virus sizes:" % len(self.data.observed_virus_sizes))
+ for size, ndata in sorted(self.data.observed_virus_sizes.items(), key=lambda x:x[0]):
+ print("\t%4d: %7d times" % (size, ndata))
+
+ def analyze_remerge(self):
+ relevant = list(filter(lambda r : r.is_parent_child, self.data.remerging.values()))
+ durations = list(map(lambda r : r.end_time - r.begin_time, relevant))
+ print(fit_gaussian(durations))
+ waittimes = list(map(lambda r : r.begin_time - max(r.birth1, r.birth2), relevant))
+ print(fit_gaussian(waittimes))
+